hw05-Rmd

Part 1: Factor management

With the data set of your choice, after ensuring the variable(s) you’re exploring are indeed factors, you are expected to:

1. Drop factor / levels;
2. Reorder levels based on knowledge from data.

suppressPackageStartupMessages(library(gapminder))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(knitr))
suppressPackageStartupMessages(library(kableExtra))
suppressPackageStartupMessages(library(plotly))
suppressPackageStartupMessages(library())

levels(gapminder$continent)

## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

nlevels(gapminder$continent)

## [1] 5

gapminder_drop_oceania <- gapminder %>% filter(continent != "Oceania")
levels(gapminder_drop_oceania$continent) #even though we filtered it out, it is not dropped yet

## [1] "Africa"   "Americas" "Asia"     "Europe"   "Oceania"

gapminder_drop_oceania2 <- gapminder %>% filter(continent != "Oceania") %>% droplevels()
levels(gapminder_drop_oceania2$continent) #now you can see we have dropped the Oceania level

## [1] "Africa"   "Americas" "Asia"     "Europe"

gap_Americas_2002 <- gapminder %>% 
  filter(year == 2002, continent == "Americas")

gap_Americas_2002 %>% 
    mutate(country = fct_reorder(country, desc(lifeExp))) %>% 
    ggplot(aes(lifeExp, country)) + 
    geom_point(colour = "Red") +
    labs(y = "Country", x = "Life Expectancy")

Part 2: File I/O

Experiment with one or more of write_csv()/read_csv() (and/or TSV friends), saveRDS()/readRDS(), dput()/dget(). Create something new, probably by filtering or grouped-summarization of Singer or Gapminder. I highly recommend you fiddle with the factor levels, i.e. make them non-alphabetical (see previous section). Explore whether this survives the round trip of writing to file then reading back in.

We will now write the file to the working directory:

write_csv(gap_Americas_2002,"Americas_2002", col_names = TRUE)

We can also read it back using the read_csv function:

read_back <- read_csv("Americas_2002")

## Parsed with column specification:
## cols(
##   country = col_character(),
##   continent = col_character(),
##   year = col_integer(),
##   lifeExp = col_double(),
##   pop = col_integer(),
##   gdpPercap = col_double()
## )

To prove that it worked lets check:

knitr::kable(read_back)

kable(read_back) %>%
  kable_styling("striped", full_width = F)

Part 3: Visualization design

Remake at least one figure or create a new one, in light of something you learned in the recent class meetings about visualization design and color. Maybe juxtapose your first attempt and what you obtained after some time spent working on it. Reflect on the differences. If using Gapminder, you can use the country or continent color scheme that ships with Gapminder. Consult the dimensions listed in All the Graph Things.

Then, make a new graph by converting this visual (or another, if you’d like) to a plotly graph. What are some things that plotly makes possible, that are not possible with a regular ggplot2 graph?

Here is a plot I created early in the semester:

ggplot(gapminder, aes(gdpPercap, lifeExp)) +
  scale_x_log10() +
  geom_point(colour = "blue", alpha=0.2)

Let’s try to revamp this:

revamp <- gapminder %>% 
     ggplot(aes(gdpPercap, lifeExp)) +
     geom_point(aes(colour=pop), alpha=0.2) +
     scale_x_log10( ) +
     scale_colour_distiller(
         trans   = "log10",
         breaks  = 5^(1:5),
         palette = "Blue" 
     ) + theme_light() + labs(title="Life Expectancy and GDP Per Capita") +
  ylab("Life Expectancy") +
  xlab("GDP Per Capita") +
  facet_wrap(~ continent) +
     scale_y_continuous(breaks=10*(1:10))

## Warning in pal_name(palette, type): Unknown palette Blue

ggplotly(revamp)